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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Higher spin partition functions via the quasinormal mode method in de Sitter quantum gravity

In this note we compute the 1-loop partition function of spin- s s fields on Euclidean de Sitter space S^{2n+1} S 2 n + 1 using the quasinormal mode method. Instead of computing the quasinormal mode frequencies from scratch, we use the analytic continuation prescription L_{\text{AdS}}\to iL_{\text{dS}} L AdS → i L dS , appearing in the dS/CFT correspondence, and Wick rotate the normal mode frequencies of fields on thermal \text{AdS}_{2n+1} AdS 2 n + 1 into the quasinormal mode frequencies of fields on de Sitter space. We compare the quasinormal mode and heat kernel methods of calculating 1-loop determinants, finding exact agreement, and furthermore explicitly relate these methods via a sum over the conformal dimension. We discuss how the Wick rotation of normal modes on thermal \text{AdS}_{2n+1} AdS 2 n + 1 can be generalized to calculating 1-loop partition functions on the thermal spherical quotients S^{2n+1}/\mathbb{Z}_{p} S 2 n + 1 / ℤ p . We further show that the quasinormal mode frequencies encode the group theoretic structure of the spherical spacetimes in question, analogous to the analysis made for thermal AdS in [1-3] .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

An analytic result for the 0 → ggHHH amplitude

We present a fully analytic calculation of the leading-order one-loop amplitude for triple Higgs production via gluon fusion, gg → HHH, retaining full dependence on the mass of the heavy quark circulating in the loop. This amplitude provides a direct probe of the triple and quartic Higgs self-couplings, the measurement of which is a central goal of current and future colliders. The amplitude can be presented in compact form thanks to the use of analytic reconstruction techniques, based on finite-field and p-adic evaluations, multivariate partial fraction decompositions, and primary decompositions to identify common numerator factors. Although full analytic results are given in the text and in the supplementary material, the main thrust of this paper is to further test and illustrate these analytic reconstruction techniques in a concrete physical example. Our results provide a compact and efficient representation of the matrix element for this process, enabling evaluations that are more than an order of magnitude faster than existing numerical alternatives. Full analytic control of the leading-order, loop-induced amplitude is an important step towards handling more complex 2-loop or real-radiation corrections to this and related processes.

Campbell, John M. [Fermilab] (ORCID:00000002136193↗
DOE OSTI2020

Kinetic Simulations of Microstructural Evolution (KSOME)

"Kinetic simulations of microstructural evolution" (KSOME) is an object Kinetic Monte Carlo (OMKC) simulation code. It is developed to simulate microstructural evolution in materials during irradiation. KSOME is developed from ground up with priority given to the flexibility, ease of upgradability, and computation efficiency, in that order. With previously developed simulation codes, various processes (more precisely, the diffusion-reaction processes) that can occur in a system were hardwired into the code. Hence, to perform higher fidelity simulations, a significant coding effort was required every time to incorporate either a more accurate description of existing processes or new processes. With KSOME, the necessity for frequent code upgrades whenever there is a need to perform higher fidelity simulations is either eliminated or reduced significantly. In KSOME, the categories of various diffusion-reaction processes and the data-management system are hardwired, while the execution of the diffusion-reaction processes specific to a system is specified via input files. In the present version of KSOME, the analytical expression required to calculate defect interaction radii are inputted via a text file. However, the ability to parse mathematical expressions from a text file can be extended to input the analytical expression to calculate other defect properties; including the expressions to calculate the change in the migration barriers of mobile defects due to long-range interaction with extended defects

Nandipati, Giridhar [Pacific Northwest National La↗
DOE OSTI2018

Theoretical Expressions for the Ascent Rate of Moist Deep Convective Thermals

An approximate analytic expression is derived for the ratio λ of the ascent rate of moist deep convective thermals and the maximum vertical velocity within them; λ is characterized as a function of two nondimensional buoyancy-dependent parameters y and h and is used to express the thermal ascent rate as a function of the buoyancy field. The parameter y characterizes the vertical distribution of buoyancy within the thermal, and h is the ratio of the vertically integrated buoyancy from the surface to the thermal top and the vertical integral of buoyancy within the thermal. Theoretical λ values are calculated using values of y and h obtained from idealized numerical simulations of ascending moist updrafts and compared to λ computed directly from the simulations. The theoretical values of [Formula: see text] 0.4–0.8 are in reasonable agreement with the simulated λ (correlation coefficient of 0.86). These values are notably larger than the [Formula: see text] from Hill’s (nonbuoyant) analytic spherical vortex, which has been used previously as a framework for understanding the dynamics of moist convective thermals. The relatively large values of λ are a result of net positive buoyancy within the upper part of thermals that opposes the downward-directed dynamic pressure gradient force below the thermal top. These results suggest that nonzero buoyancy within moist convective thermals, relative to their environment, fundamentally alters the relationship between the maximum vertical velocity and the thermal-top ascent rate compared to nonbuoyant vortices. Implications for convection parameterizations and interpretation of the forces contributing to thermal drag are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

An improved understanding of the roles of atomic processes and power balance in divertor target ion current loss during detachment

The process of divertor detachment, whereby heat and particle fluxes to divertor surfaces are strongly diminished, is required to reduce heat loading and erosion in a magnetic fusion reactor to acceptable levels. In this paper, the physics leading to the decrease of the total divertor ion current ($I_t$), or ‘roll-over’, is experimentally explored on the TCV tokamak through characterization of the location, magnitude and role of the various divertor ion sinks and sources including a complete analysis of particle and power balance. These first measurements of the profiles of divertor ionisation and hydrogenic radiation along the divertor leg are enabled through novel spectroscopic techniques. Over a range in TCV plasma conditions (plasma current and electron density, with/ without impurity-seeding) the $I_t$ roll-over is ascribed to a drop in the divertor ion source; recombination remains small or negligible farther into the detachment process. Further, the ion source reduction is driven by both a reduction in the power available for ionization, $P_{\text{recl}}$, and concurrent increase in the energy required per ionisation, $E_{\text{ion}}$: this effect of power available on the ionization source is often described as ‘power starvation’ (or ‘power limitation’). The detachment threshold is found experimentally (in agreement with analytic model predictions) to be ~$P_{\text{recl}}/I_tE_{\text{ion}}$ ~ 2, corresponding to a target electron temperature, $T_t ~ E_{\text{ion}}/γ$ where $\gamma$ is the sheath transmission coefficient. The target pressure reduction, required to reduce the target ion current, is driven both by volumetric momentum loss as well as upstream pressure loss. The measured evolution through detachment of the divertor profile of various ion sources/ sinks as well as power losses are quantitatively reproduced through full 2D SOLPS modelling through the detachment process as the upstream density is varied.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Large-Scale Trajectory Analysis via Feature Vectors

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

58 GEOSCIENCES↗
DOE OSTI2012

CLIC Crab Cavity Design Optimisation for Maximum Luminosity

The bunch size and crossing angle planned for CERN's compact linear collider CLIC dictate that crab cavities on opposing linacs will be needed to rotate bunches of particles into alignment at the interaction point if the desired luminosity is to be achieved. Wakefield effects, RF phase errors between crab cavities on opposing linacs and unpredictable beam loading can each act to reduce luminosity below that anticipated for bunches colliding in perfect alignment. Unlike acceleration cavities, which are normally optimised for gradient, crab cavities must be optimised primarily for luminosity. Accepting the crab cavity technology choice of a 12 GHz, normal conducting, travelling wave structure as explained in the text, this paper develops an analytical approach to optimise cell number and iris diameter.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Isotherm Model for Moisture-Controlled CO 2 Sorption

Moisture-controlled sorption of CO 2 , the basis for moisture-swing CO 2 capture from air, is a novel phenomenon observed in strong-base anion exchange materials. Prior research has shown that Langmuir isotherms provide an approximate fit to moisture-controlled CO 2 sorption isotherm data. However, this fit still lacks a governing equation derived from an analytic model. As such, in this paper we derive an analytic form for an isotherm equation from a bottom-up approach, starting with a fundamental theory for an alkali liquid. In the range of interest relevant to CO 2 capture from air, an isotherm equation for an alkali liquid reduces to a simple analytic form with a single parameter, $K_{\text{eq}}$. In the limit $K_{\text{eq}} \gg 1$, a 2nd order approximation simplifies to a Langmuir isotherm that, however, deviates from experimental data. The isotherm theory for an alkali liquid has been generalized to a strong-base anion exchange material. In a strong-base anion exchange material, water concentration inside a sorbent, [H 2 O], is not large enough to be regarded as constant, which allows us to extend $K_{\text{eq}}$ to $K_{\text{eq(AEM)eff}} = K_{\text{eq(AEM)}} \times$ [H 2 O]$^{–n}$ according to the law of mass action. The final isotherm formula has been validated by experimental data from the literature. For a moisture-controlled CO 2 sorbent, $K_{\text{eq(AEM)eff}}$ varies significantly with moisture content of the sorbent. Depending on moisture level, the observed $K_{\text{eq(AEM)eff}}$ in a specific sorbent ranges from a few times to a few thousand times the value of $K_{\text{eq}}$ of a 2 mol L –1 alkali liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Method and system for information retrieval and aggregation from inferred user reasoning

The visual analytic system enables information retrieval within large text collections. Typically, users have to directly and explicitly query information to retrieve it. With this system and process, the reasoning of the user is inferred from the user interaction they perform in a visual analytic tool, and the appropriate information to query, process, and visualize is systematically determined.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Safety Analytics Forecasting and Evaluation Reporting (SAFER)

The SAFER Sandbox is a web application for testing various types of data visualization tools that analyze safety data across national laboratories. By enabling the user to visualize this data through visual and textual analyses, the SAFER Sandbox helps to facilitate the research and support for the National Nuclear Security Administration (NNSA) Office of Safety, Infrastructure and Operations (NA-50). Safety data across the national laboratory complex is not currently analyzed in one location. This prohibits administrators from viewing safety data trends and making large scale decisions based on a holistic view of the data. By implementing data analytics web services such as representative text summary, noun phrase extraction and graph exploration, users can view and explore safety data based on search queries. The data visualizations in the SAFER Sandbox reveal trends and point to items of concern based on safety data. The SAFER Sandbox is a testing ground for these visualizations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2012

Revisions to the SRCC Rating Process for Solar Water Heaters: Preprint

In the United States, annual performance ratings for solar water heaters are computed with component-based simulation models driven by typical meteorological year weather and specified water draw. Changes in the process are being implemented to enhance credibility through increased transparency and accuracy. Changes to the process include using a graphical rather than text-based model-buildingtool, performing analytical tests on all components and systems, checking energy balances on every component, loop, and system at every time step, comparing the results to detect outliers and potential errors, and documenting the modeling process in detail. Examples of changes in ratings are shown, along with analytical and comparative testing results.

14 SOLAR ENERGY↗
DOE OSTI2012

Revisions to the SRCC Rating Process for Solar Water Heaters

In the United States, annual performance ratings for solar water heaters are computed with component-based simulation models driven by typical meteorological year weather and specified water draw. Changes in the process are being implemented to enhance credibility through increased transparency and accuracy. Changes to the process include using a graphical rather than text-based model-building tool, performing analytical tests on all components and systems, checking energy balances on every component, loop, and system at every time step, comparing the results to detect outliers and potential errors, and documenting the modeling process in detail. Examples of changes in ratings are shown, along with analytical and comparative testing results.

14 SOLAR ENERGY↗
DOE OSTI2013

Extreme Scale Visual Analytics

Given the scale and complexity of today s data, visual analytics is rapidly becoming a necessity rather than an option for comprehensive exploratory analysis. In this paper, we provide an overview of three applications of visual analytics for addressing the challenges of analyzing climate, text streams, and biosurveilance data. These systems feature varying levels of interaction and high performance computing technology integration to permit exploratory analysis of large and complex data of global significance.

Steed, Chad A↗
DOE OSTI2017

The Impact of Streaming Data on Sensemaking with Mixed-Initative Visual Analytics

Visual data analysis helps people gain insights into data via interactive visualizations. People generate and test hypotheses and questions about data in context of the domain. This process can generally be referred to as sensemaking. Much of the work on studying sensemaking (and creating visual analytic techniques in support of it) has been focused on static datasets. However, how do the cognitive processes of sensemaking change when data are changing? Further, what implication for design does this create for mixed-initiative visual analytics systems? This paper presents the results of a user study analyzing the impact of streaming data on sensemaking. To perform this study, we developed a mixed-initiative visual analytic prototype, the Streaming Canvas, that affords the analysis of streaming text data. We compare the sensemaking process of people using this tool for a static and streaming dataset. We present the results of this study and discuss the implications on future visual analytic systems that combine machine learning and interactive visualization to help people make sense of streaming data.

visualization↗
DOE OSTI2016

MO-DE-BRA-06: 3D Image Acquisition and Reconstruction Explained with Online Animations

Purpose: Understanding the principles of 3D imaging and image reconstruction is fundamental to the field of medical imaging. Clinicians, technologists, physicists, patients, students, and inquisitive minds all stand to benefit from greater comprehension of the supporting technologies. To help explain the basic principles of 3D imaging, we developed multi-frame animations that convey the concepts of tomographic imaging. The series of free (gif) animations are accessible online, and provide a multimedia introduction to the main concepts of image reconstruction. Methods: Text and animations were created to convey the principles of analytic tomography in CT, PET, and SPECT. Specific topics covered included: principles of sinograms/image data storage, forward projection, principles of PET acquisitions, and filtered backprojection. A total of 8 animations were created and presented for CT, PET, and digital phantom formats. In addition, a free executable is also provided to allow users to create their own tomographic animations – providing an opportunity for interaction and personalization to help foster user interest. Results: Tutorial text and animations have been posted online, freely available to view or download. The animations are in first position in a google search of “image reconstruction animations”. The website currently receives approximately 200 hits/month, from all over the world, and the usage is growing. Positive feedback has been collected from users. Conclusion: We identified a need for improved teaching tools to help visualize the (temporally variant) concepts of image reconstruction, and have shown that animations can be a useful tool for this aspect of education. Furthermore, posting animations freely on the web has shown to be a good way to maximize their impact in the community. In future endeavors, we hope to expand this animated content, to cover principles of iterative reconstruction, as well as other phenomena relating to imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Interactive Visual Analysis of High Throughput Text Streams

The scale, velocity, and dynamic nature of large scale social media systems like Twitter demand a new set of visual analytics techniques that support near real-time situational awareness. Social media systems are credited with escalating social protest during recent large scale riots. Virtual communities form rapidly in these online systems, and they occasionally foster violence and unrest which is conveyed in the users language. Techniques for analyzing broad trends over these networks or reconstructing conversations within small groups have been demonstrated in recent years, but state-of- the-art tools are inadequate at supporting near real-time analysis of these high throughput streams of unstructured information. In this paper, we present an adaptive system to discover and interactively explore these virtual networks, as well as detect sentiment, highlight change, and discover spatio- temporal patterns.

sentiment analysis↗
DOE OSTI2011

Analytic Steering: Inserting Context into the Information Dialog

An analyst’s intrinsic domain knowledge is a primary asset in almost any analysis task. Unstructured text analysis systems that apply un-supervised content analysis approaches can be more effective if they can leverage this domain knowledge in a manner that augments the information discovery process without obfuscating new or unexpected content. Current unsupervised approaches rely upon the prowess of the analyst to submit the right queries or observe generalized document and term relationships from ranked or visual results. We propose a new approach which allows the user to control or steer the analytic view within the unsupervised space. This process is controlled through the data characterization process via user supplied context in the form of a collection of key terms. We show that steering with an appropriate choice of key terms can provide better relevance to the analytic domain and still enable the analyst to uncover un-expected relationships; this paper discusses cases where various analytic steering approaches can provide enhanced analysis results and cases where analytic steering can have a negative impact on the analysis process.

Steering↗
DOE OSTI2014

The VAST Challenge: History, Scope, and Outcomes: An introduction to the Special Issue

Visual analytics aims to facilitate human insight from complex data via a combination of visual representations, interaction techniques, and supporting algorithms. To create new tools and techniques that achieve this goal requires that researchers have an understanding of analytical questions to be addressed, data that illustrates the complexities and ambiguities found in realistic analytic settings, and methods for evaluating whether the plausible insights are gained through use of the new methods. However, researchers do not, generally speaking, have access to analysts who can articulate their problems or operational data that is used for analysis. To fill this gap, the Visual Analytics Science and Technology (VAST) Challenge has been held annually since 2006. The VAST Challenge provides an opportunity for researchers to experiment with realistic but not real problems, using realistic synthetic data with known events embedded. Since its inception, the VAST Challenge has evolved along with the visual analytics research community to pose more complex challenges, ranging from text analysis to video analysis to large scale network log analysis. The seven years of the VAST Challenge have seen advancements in research and development, education, evaluation, and in the challenge process itself. This special issue of Information Visualization highlights some of the noteworthy advancements in each of these areas. Some of these papers focus on important research questions related to the challenge itself, and other papers focus on innovative research that has been shaped by participation in the challenge. This paper describes the VAST Challenge process and benefits in detail. It also provides an introduction to and context for the remaining papers in the issue.

visual analytics, sensemaking, evaluation, crowdso↗